Some probabilistic generalizations of the submeasure concept.
Lipovan, Octavian (1996)
Novi Sad Journal of Mathematics
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Lipovan, Octavian (1996)
Novi Sad Journal of Mathematics
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José M. Bernardo, Javier Girón (1983)
Trabajos de Estadística e Investigación Operativa
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An elementary axiomatic foundation for decision theory is presented at a general enough level to cover standard applications of Bayesian methods. The intuitive meaning of both axioms and results is stressed. It is argued that statistical inference is a particular decision problem to which the axiomatic argument fully applies.
Isaac Levi (1981)
Trabajos de Estadística e Investigación Operativa
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Those who follow Harold Jeffreys in using improper priors together with likelihoods to determine posteriors have thought of the improper measures as probability measures of a deviant sort. This is a mistake. Probability measures are finite measures. Improper distributions generate σ-finite measures. (...)
R. Suszko (1967)
Colloquium Mathematicae
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Andrea Capotorti, Giulianella Coletti, Barbara Vantaggi (1998)
Kybernetika
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We characterize (in terms of necessary and sufficient conditions) binary relations representable by a lower probability. Such relations can be non- additive (as the relations representable by a probability) and also not “partially monotone” (as the relations representable by a belief function). Moreover we characterize relations representable by upper probabilities and those representable by plausibility. In fact the conditions characterizing these relations are not immediately deducible...
Bruce M. Hill (1980)
Trabajos de Estadística e Investigación Operativa
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Some statistical paradoxes arising from the use of non-conglomerable finitely additive distributions are discussed.
Gardner, Robert, Price, Robert (2002)
International Journal of Mathematics and Mathematical Sciences
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Romano Scozzafava (1996)
Mathware and Soft Computing
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